X-ray imagery is a widely used modality for non-destructive testing [1]}, [2]}, especially for screening illegal and smuggled items at airports, cargoes, and malls. Manual baggage inspection is a tiring task and susceptible to errors caused due to exhausting work routines and less experienced personnel. Initial systems proposed to address these problems employed conventional machine learning [3]}. Driven by hand-engineered features, these methods are only applicable to limited data and confined environmental settings [4]}. Recently, attention has turned to deep learning methods, which gave a neat boost in accuracy and generalization capacity towards screening prohibited baggage items [5]}, [6]}, [7]}, [8]}. However, deep learning methods are also prone to clutter, and occlusion [9]}. This limitation emanates from the proposal generation strategies which have been designed for the color images [10]}. Unlike RGB scans, X-ray imagery lack texture and exhibit low-intensity variations between cluttered objects. This intrinsic difference makes the region-based or anchor-based proposal generation methods such as Mask R-CNN [11]}, Faster R-CNN [12]}, RetinaNet [13]}, and YOLO [14]} less robust for detecting the cluttered contraband data [9]}. Moreover, the problem is further accentuated by the class imbalance nature of the contraband items in the real-world [10]}.
Despite the considerate strategies proposed to alleviate the occlusion and the imbalance nature [17]}, [18]}, [19]}, [20]}, [21]}, recognizing threatening objects in highly cluttered and concealed scenarios is still an open problem [22]}.
